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Publications

Publications by Ricardo Pereira Cruz

2017

Combining ranking with traditional methods for ordinal class imbalance

Authors
Cruz, R; Fernandes, K; Pinto Costa, JFP; Perez Ortiz, MP; Cardoso, JS;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive. © Springer International Publishing AG 2017.

2016

Tackling Class Imbalance with Ranking

Authors
Cruz, R; Fernandes, K; Cardoso, JS; Pinto Costa, JFP;

Publication
2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Abstract
In classification, when there is a disproportion in the number of observations in each class, the data is said to be class imbalance. Class imbalance is pervasive in real world applications of data classification and has been the focus of much research. The minority class contributes too little to the decision boundary because the learning process learns from each observation in isolation. In this paper, we discuss the application of learning pairwise rankers as a solution to class imbalance. We compare ranking models to alternatives from the literature.

2017

Constraining Type II Error: Building Intentionally Biased Classifiers

Authors
Cruz, R; Fernandes, K; Pinto Costa, JFP; Cardoso, JS;

Publication
Advances in Computational Intelligence - Lecture Notes in Computer Science

Abstract

2017

Ordinal Class Imbalance with Ranking

Authors
Cruz, R; Fernandes, K; Pinto Costa, JFP; Perez Ortiz, MP; Cardoso, JS;

Publication
Pattern Recognition and Image Analysis - Lecture Notes in Computer Science

Abstract

2017

Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation

Authors
Perez Ortiz, M; Fernandes, K; Cruz, R; Cardoso, JS; Briceno, J; Hervas Martinez, C;

Publication
Advances in Computational Intelligence - Lecture Notes in Computer Science

Abstract

2018

A Class Imbalance Ordinal Method for Alzheimer's Disease Classification

Authors
Cruz, R; Silveira, M; Cardoso, JS;

Publication
2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

Abstract
The majority of computer-Aided diagnosis methods for Alzheimer's disease (AD) from brain images either address only two stages of the disease at a time (and reduce the problem to binary classification) or do not exploit the ordinal nature of the different classes. An exception is the work by Fan et al. [1], which proposed an ordinal method that obtained better performance than traditional multiclass classification. Still, special care should be taken when data is class imbalanced, i.e. when some classes are overly represented when compared to others. Building on top of [1], this work makes use of a recently published ordinal classifier, which transforms the problem into sets of pairwise ranking problems, in order to address the class imbalance in the data [2]. Several methods were experimented with, using a Support Vector Machine as the underlying estimator. The pairwise ranking approach has shown promising results, both for traditional and imbalance metrics. © 2018 IEEE.

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